class_docstr: Base class for Flax-based pipelines.
  
  [`FlaxDiffusionPipeline`] stores all components (models, schedulers, and processors) for diffusion pipelines and
  provides methods for loading, downloading and saving models. It also includes methods to:
  
      - enable/disable the progress bar for the denoising iteration
  
  Class attributes:
  
      - **config_name** ([`str`]) -- The configuration filename that stores the class and module names of all the
        diffusion pipeline's components.
docstr: Convert a NumPy image or a batch of images to a PIL image.
docstr_tok: ["Convert","a","NumPy","image","or","a","batch","of","images","to","a","PIL","image","."]
code: def numpy_to_pil(images):
      """
      Convert a NumPy image or a batch of images to a PIL image.
      """
      if images.ndim == 3:
          images = images[None, ...]
      images = (images * 255).round().astype("uint8")
      if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          pil_images = [
              Image.fromarray(image.squeeze(), mode="L")
              for image in images
          ]
      else:
          pil_images = [Image.fromarray(image) for image in images]
  
      return pil_images
  
imports: import importlib
  import inspect
  import os
  from typing import Any, Dict, List, Optional, Union
  import flax
  import numpy
  import PIL.Image
  from flax.core.frozen_dict import FrozenDict
  from huggingface_hub import create_repo, snapshot_download
  from PIL import Image
  from tqdm.auto import tqdm
  from ..configuration_utils import ConfigMixin
  from ..models.modeling_flax_utils import FLAX_WEIGHTS_NAME, FlaxModelMixin
  from ..schedulers.scheduling_utils_flax import SCHEDULER_CONFIG_NAME, FlaxSchedulerMixin
  from ..utils import CONFIG_NAME, DIFFUSERS_CACHE, BaseOutput, PushToHubMixin, http_user_agent, is_transformers_available, logging
label_desc: Use image node_id 6 for calling the FlaxDiffusionPipeline obj's underlying member method code with example usage: obj.numpy_to_pil(images) and returns: pil_images